Showing 2024 · cs.ROShow all
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cs.RO2024
Variable Time Step Reinforcement Learning for Robotic Applications
Dong Wang, Giovanni Beltrame
Traditional reinforcement learning (RL) generates discrete control policies, assigning one action per cycle. These policies are usually implemented as in a fixed-frequency control…
cs.RO2024
Reinforcement Learning with Elastic Time Steps
Dong Wang, Giovanni Beltrame
Traditional Reinforcement Learning (RL) policies are typically implemented with fixed control rates, often disregarding the impact of control rate selection. This can lead to ineff…
cs.RO2024
Deployable Reinforcement Learning with Variable Control Rate
Dong Wang, Giovanni Beltrame
Deploying controllers trained with Reinforcement Learning (RL) on real robots can be challenging: RL relies on agents' policies being modeled as Markov Decision Processes (MDPs), w…